Month: June 2017

Variables can be described as either quantitative or qualitative.
Quantitative variables have a numerical value, e.g. a person’s income, or the price of a house.Qualitative variables have a values taken from one of different classes or categories. E.g., a person’s gender (male or female), the type of house purchased (villa, flat, penthouse, …) the colour of the eye (brown, blue, green) or a cancer diagnosis.

Linear regression predicts a continuous variable but sometime we want to predict a categorical variable, i.e. a variable with a small number of possible discrete outcomes, usually unordered (there is no order among the outcomes).

This kind of problems are called Classification.

Classification

Given a feature vector X and a qualitative response y taking values from one fixed set, the classification task is to build a function f(X) that takes as input the feature vector X and predicts its value for y.
Often we are interested also (or even more) in estimating the probabilities that X belongs to each category in C.
For example, it is more valuable to have the probability that an insurance claim is fraudulent, than if a classification is fraudulent or not.

There are many possible classification techniques, or classifiers, available to predict a qualitative response.

Welcome!

This is my personal blog, where I write about what I learned, mostly about software, project management and machine learning.
Why this name? The blog should help me to navigate into the future using (and not forgetting) the past experiences.
From Europe to the world.